In the era of data-intensive research, it is more important than ever to offer data, information and knowledge in a machine-readable format. Ideally, we can provide access to semantically integrated data content and combine the data with information and knowledge in the form of ontologies, databases or linked data. By doing so, we can make heterogeneous sources increasingly suitable for automatic interpretation by artificial intelligence (AI) methods.
With a primary focus on life science data (mainly biomedical and agricultural data), our research group develops a range of text and data mining techniques, including:
- semantic search in publications,
- extracting information and knowledge from literature and databases and
- data mining and machine learning/deep learning applications in heterogeneous data sources
We also develop resources, methods and tools for research data management, all designed to support the semantic integration and reusability of data, and specifically the generation of FAIR data. Additionally, we take steps to enhance the availability of machine-readable knowledge. These include, for example, application-specific semantic lookup platforms, automatic data annotation workflows and various other semantic web techniques.
The research group consists of colleagues from the University of Bonn and ZB MED – Information Centre for Life Sciences.
Professor Juliane Fluck heads up the Knowledge Management research group at ZB MED.
